ML Engineer

Coherent Solutions is a digital product engineering company focused on empowering business success. Our global team of 1700+ talented professionals collaborate seamlessly to deliver innovative solutions that drive measurable business impact. Headquartered in Minneapolis, USA, the company’s core competencies across 10 locations worldwide include product software development, IT consulting, data and analytics, machine learning, mobile app development, DevOps, Salesforce, and more.
Project Description
You will work with senior engineers on internal classical machine learning initiatives. The role combines structured learning, practical delivery, and gradual ownership of increasingly complex tasks.
Technologies
Python, pandas, NumPy, scikit-learn, Jupyter
SQL
XGBoost, LightGBM
MLflow
FastAPI
Git, Docker, pytest
What You'll Do
Prepare and validate structured datasets for machine learning tasks, including cleaning, transformation, encoding, scaling, and missing-value handling;
Conduct exploratory data analysis to identify patterns, data quality issues, and opportunities for modeling;
Build, test, and compare ML models for tasks such as regression, classification, clustering, dimensionality reduction, and anomaly detection;
Develop and select features with guidance from senior engineers;
Run experiments, tune model parameters, and evaluate results using appropriate validation approaches and metrics;
Help maintain reproducible ML pipelines, notebooks, experiment tracking, and technical documentation;
Write and maintain automated tests for data and model-related code;
Support the delivery of models through REST APIs or batch-processing workflows;
Document model assumptions, results, limitations, and technical decisions;
Work closely with senior engineers, incorporate feedback, and gradually take ownership of more complex tasks;
Job Requirements
1+ year of hands-on Python development experience, including experience with pandas, NumPy, scikit-learn, and Jupyter;
A strong foundation in statistics, probability, and linear algebra, plus exploratory data analysis skills;
An understanding of supervised and unsupervised learning, and practical experience with regression, classification, clustering, dimensionality reduction, and anomaly detection;
Experience preparing structured datasets, including cleaning, transformation, encoding, scaling, and missing-value handling, plus practical feature engineering and feature-selection skills;
An understanding of train/validation/test splits, cross-validation, data leakage, overfitting, and regularization; the ability to select appropriate evaluation metrics, compare models, and perform error analysis;
Familiarity with hyperparameter tuning and reproducible ML pipelines;
SQL skills for data extraction and analysis;
The ability to expose models through REST APIs or batch-processing workflows;
Familiarity with Git, automated testing, Docker, and basic model monitoring;
The ability to explain model behavior, assumptions, limitations, and results;
English at B2 level or higher;
Nice To Have
XGBoost, time-series analysis, recommendation systems, MLflow, model interpretability, cloud services, production ML monitoring, and GenAI, LLM, RAG, prompt-engineering, or agent-development experience;
What Do We Offer
The global benefits package includes:
Technical and non-technical training for professional and personal growth;
Internal conferences and meetups to learn from industry experts;
Support and mentorship from an experienced employee to help you professional grow and development;
Health insurance;
Sports activities to promote a healthy lifestyle;
Flexible work options, including remote and hybrid opportunities;
Referral program for bringing in new talent;
Work anniversary program and additional vacation days.
If this opportunity excites you, send us your resume!
Please, note, that we will consider all the applications with due respect, but only shortlisted candidates will be contacted.
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Work arrangement
Yes